@inproceedings{dhar-etal-2025-ju,
    title = "{JU}-{CSE}-{NLP}{'}s Cascaded Speech to Text Translation Systems for {IWSLT} 2025 in {I}ndic Track",
    author = "Dhar, Debjit  and
      Lahiri, Soham  and
      Mondal, Tapabrata  and
      Bandyopadhyay, Sivaji",
    editor = "Salesky, Elizabeth  and
      Federico, Marcello  and
      Anastasopoulos, Antonis",
    booktitle = "Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria (in-person and online)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.iwslt-1.18/",
    pages = "201--205",
    ISBN = "979-8-89176-272-5",
    abstract = "This paper presents the submission of the Jadavpur University Computer Science and Engineering Natural Language Processing (JU-CSENLP) Laboratory to the International Conference on Spoken Language Translation (IWSLT) 2025 Indic track, addressing the speech-to-text translation task in both English-to-Indic (Bengali, Hindi, Tamil) and Indic-to-English directions. To tackle the challenges posed by low resource Indian languages, we adopt a cascaded approach leveraging state-of-the-art pre-trained models. For English-to-Indic translation, we utilize OpenAI{'}s Whisper model for Automatic Speech Recognition (ASR), followed by the Meta{'}s No Language Left Behind (NLLB)-200-distilled-600M model finetuned for Machine Translation (MT). For the reverse direction, we employ the AI4Bharat{'}s IndicConformer model for ASR and IndicTrans2 finetuned for MT. Our models are fine-tuned on the provided benchmark dataset to better handle the linguistic diversity and domain-specific variations inherent in the data. Evaluation results demonstrate that our cascaded systems achieve competitive performance, with notable BLEU and chrF++ scores across all language pairs. Our findings highlight the effectiveness of combining robust ASR and MT components in a cascaded pipeline, particularly for low-resource and morphologically rich Indian languages."
}